SPS Webinar: 21 September 2022 - IEEE OJSP article on Compressed Sensing

Date: September 21, 2022
Time: 9:30 AM ET (New York Time)
Title: Tapestry: A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
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This webinar presents ‘Tapestry’, a single-round pooled testing method with application to COVID-19 testing using quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits, at clinically acceptable false positive or false negative rates. Tapestry combines ideas from compressed sensing and combinatorial group testing to create a new kind of algorithm that is very effective in deconvoluting pooled tests. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. We prove that for exact recovery of k infected samples out of n ≫ k being tested, Tapestry needs only O(k log n) tests with high probability, using random binary matrices. However, we use deterministic binary pooling matrices based on Kirkman Triple Systems, which balance between good reconstruction properties and matrix sparsity for ease of pooling while requiring fewer tests in practice. This enables large savings using Tapestry at low prevalence rates while maintaining viability at prevalence rates as high as 9.5%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We evaluate Tapestry in simulations with synthetic data obtained using a novel noise model for RT-PCR, and validate it in wet lab experiments with oligomers in quantitative RT-PCR assays


Sabyasachi Ghosh

Dr. Sabyasachi Ghosh received the B.Tech. degree in computer science and engineering from Indian Institute of Technology Kanpur, Kanpur, UP, India in 2008 and the M.S. degree in electrical engineering from University of Southern California, Los Angeles, CA, U.S.A. in 2011. He is currently pursuing the Ph.D. degree in computer science and engineering at Indian Institute of Technology Bombay, Mumbai, MH, India.

He worked as a backend software engineer at Riverbed Technology from 2011 to 2014, and a lead engineer at Shop101 in 2015. He was a Research Assistant at Tata Institute of Fundamental Research in 2016, and at Indian Institute of Technology Bombay from 2016 to 2017.

Dr. Ghosh’s research focuses on developing Group Testing and Compressed Sensing methods for application in novel areas, such as COVID-19 testing and Deep Learning. His broader research interests include Artificial Intelligence, Machine Learning, Reinforcement Learning, and Mathematical Biology.